A new calibration approach to graph-based semantic segmentation

• Ano de defesa: 2018
• Autor(a) principal: Riva, Mateus
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Computer vision; Graph-based segmentation; Imageamento de ressonância magnética; Magnetic resonance imaging; Métodos estruturais; Segmentação baseada em grafos; Structural methods; VIsão computacional
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/45/45134/tde-25092019-133337/

We introduce a calibration method for semantic segmentation of images utilizing statistical-relational graphs (SRGs), with a particular focus on pediatric Magnetic Resonance Imaging (MRI). The SRG provides a representation of a structured scene, describing both the attributes of each object of interest and the nature of their relationships, such as relative position in space. Each vertex in the graph represents an object of interest and each edge represents the relationship between two objects. Semantic segmentation can thus be performed by matching an SRG built from an observed image to a previously-built model SRG. We develop a calibration method for assessing the quality of SRG segmentation given a set of parameters, as well as an exploration of several sets of parameters applied to MRI. We present the validity and usefulness of the calibration technique, along with preliminary results on real MRI data segmentation. We additionally discuss future work on improving real data SRG-based segmentation.